Machine learning bias is a term used to describe when an algorithm produces results that are not correct because of some inaccurate assumptions made during one of the machine learning process steps.
To develop any machine learning process, the data scientist needs to go through a set of steps, from collecting the data, cleaning it, training the algorithm, and then deploying it. This process is prone to error; if one occurred in any of the steps, it would mitigate through the entire process, causing its effect to magnify in the final results.
All subfields of Data Science, whether it be machine learning, natural language processing, or any other subfield, depend on data. They all depend on the quality and quantity of datasets used to build, train and develop their core algorithms. Hence, poor quality data or faulty ones can lead to inaccurate predictions and overall bad results.
There are various causes of bias in Machine Learning applications. As data scientists, it’s part of our job to do our best to reduce and prevent the causes of bias in their models. The best way to prevent bias is to understand its cause fully. Once the cause has been identified, some actions can be taken to eliminate it and remove its effects entirely.
This article will go through the 5 main types of machine learning bias, why they occur, and how to reduce their effect.
№1: Algorithmic bias
Algorithmic bias is the error that occurs when the algorithm at the core of the machine learning process is faulty or inappropriate for the current application. Algorithmic bias can be spotted when the application starts giving wrong results for a specific group of people (input cases).
If your algorithm gives different results for almost identical cases, then maybe you need to get back and recheck your algorithm and if it’s a good fit for the problem at hand. Algorithmic bias may be either intentional or unintentional. It could be the result of technical issues within the core of the algorithm or a wrongful choice of algorithms in the first place.
№2: Sample bias
Another cause for bias in machine learning applications is sample bias. This type of bias results from an error in the early stages of application development, which is the collection and cleaning of data. Data is the core of any machine learning application; after all, the algorithm can’t learn what it didn’t see.
If the developer chose a wrong sample, one that is small in size, or contains many faulty data points, or doesn’t represent the entire data pool, to train their model, the results will be inaccurate for data points that differ from this sample.
Luckily, sample bias is not that complex to fix; you can try using a larger, more diverse dataset to train your model. You can train it multiple times, observe its behavior, and finetune the parameters to reach the best answer.
№3: Prejudice bias
You might have the correct algorithm for your problem, and you did your best to choose the best sample of data you could get, but still, your results are biased. One reason that could happen is due to prejudice bias.
Prejudice bias is often the result of the data being biased in the first place. The data you extracted and used to train your model may have preexisting bais, such as stereotypes and faulty case assumptions. So, using this data will always result in biased results no matter what algorithm you try to use.
Prejudice bias is quite difficult to solve; you can try to use an entirely new dataset, try to modify the data to eliminate any existing biases.
№4: Measurement bias
Measurements bias is probably the type of bias that occurs very early in the development process, the data collection stage of the process. If this data that the model’s performance and accuracy fully depend is inaccurate, nothing in the process’s remaining steps will be.
This data is often the result of some computations and measurements that are done, either by a human or a computer, and then stored in a database. If these computations measurements are faulty, they will result in erroneous data points affecting that will be fed to the model to train it and develop it.
№5: Exclusion bias
Choosing the correct dataset to train and build your model is not an easy task. One challenge that you may face while doing so is trying to avoid exclusion bias. Exclusion bias happens when important data points are excluded from the training dataset, and hence the resulted model doesn’t consider them.
Takeaways
Both humans and algorithms are error-prone and biased. However, that doesn’t mean that our models need to be biased as well. The technology around us is making most decisions for us, decisions on what to buy next, what school is better for our kids, what city is safer to move into, whether or not our next loan request is approved, and many many more.
These systems, however, can be biased depending on the data used to build them and the person building them as well. That’s why it’s an essential step of any machine learning application development to try to reduce and eliminate bias as much as we can. And to successfully do that, we need to understand why bias occurs in the first place, its types, and where each type occurs in the development process.
Finding and resolving the cause of a bias in any machine learning application is not an exact Science; I prefer to think of it as a form of art, a skill that will only get better if you build more projects, interact with more data and resolve different types of bias.
Considering how our dependency on data grows with every passing day, the skill of understanding the cause of bias in technical systems and how to eliminate them will continue to be a critical skill that every data scientist should work on, develop and hone to excel in their future careers.